SwinIBSR: Towards real-world infrared image super-resolution

Abstract

Traditional infrared image super-resolution (SR) methods often fall short in real-world scenarios, particularly when dealing with images degraded by various forms of noise and artifacts. Addressing this gap, this paper presents SwinIBSR, a blind SR approach optimized for real-world infrared imaging conditions. SwinIBSR introduces a practical degradation model, specifically designed to simulate the complex degradation processes encountered in real-world infrared imaging. Additionally, a mixed training strategy using both infrared and visible light datasets greatly enhances the network’s generalization capabilities, leading to the production of high-quality SR images. Building upon the Transformer-based architecture of SwinIR, SwinIBSR is adept at learning intricate mappings from low-resolution to high-resolution images. Our comprehensive experiments validate the effectiveness of SwinIBSR, showcasing its outstanding performance in processing real-world infrared images and its notable superiority over existing methods regarding visual quality. This work contributes to the field of infrared image SR, offering insights in enhancing SR techniques for practical applications.